Link prediction using metadata. When the network structure is partially observable, several methods such as a matrix factorization-aided approach [1]} were proposed to leverage node features for link prediction. Our study is rather related to the inductive link prediction problem, the so-called cold-start link prediction, in which no initial topological information is available. To achieve satisfactory prediction performance, deep learning-based techniques such as G2G [2]} and DEAL [3]} were presented, which use a deep encoder that embeds each node in a given network as Gaussian distributions and aligning both structure and feature embeddings, respectively.
